Noise detection and localization

ABSTRACT

Various techniques include detecting noise in a network, identifying the type of noise in the network, localizing noise in the network, determining noise scores for network devices, and/or determining likelihoods that particular devices are causing noise and/or are in proximity of a point of entry of noise into the network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. Pat. No. 11,362,744, entitled“NOISE DETECTION AND LOCALIZATION” filed on Feb. 24, 2020, which claimspnority to U.S. Provisional Patent Application Ser. No. 62/809,676,entitled “UPSTREAM NOISE DETECTION AND LOCAUZATION′″ filed on Feb. 24,2019, U.S. Provisional Patent Application Ser. No. 62/908,306, entitled“NOISE DETECTION AND LOCALIZATION” filed on Sep. 30, 2019, and U.S.Provisional Patent Application Ser. No. 62/972,550, entitled “NOISEDETECTION AND LOCAUZATION” filed on Feb. 10, 2020, the contents of whichare hereby incorporated by reference in their entirety.

FIELD

The present disclosure relates generally to analysis of noise, and morespecifically to noise detection and localization.

BACKGROUND

Service providers (e.g., operators) provide customers (e.g.,subscribers) with services, such as multimedia, audio, video, telephony,data communications, wireless networking, and wired networking. Serviceproviders provide such services by deploying one or more electronicdevices at their customers' premises, and then connecting the deployedelectronic device to the service provider's network or infrastructure.The deployed electronic devices are often called Customer PremiseEquipment (CPE). For example, a cable company delivers media services tocustomers by connecting an electronic device, such as a set-top box or acable modem, located at customer's premise to the cable company'snetwork. This CPE is the device that the service provider uses todeliver the service to the customer.

Networks, such as those maintained by service providers or theircustomers, may have noise cause by impairments, which can cause servicedegradation and customer dissatisfaction. Examples of impairmentsinclude loose or corroded connectors, damaged cables, and floodedamplifiers. Over time, as the network ages, the severity and number ofimpairments increase. Service providers face challenges in identifyingthe type of noise in the network and localizing the noise in the networkto fix the impairments in a timely manner so as to limit the impacts ofservice degradation or outage of their customers.

BRIEF SUMMARY

Some techniques for identifying and prioritizing impairments of anetwork, however, are unreliable or inaccurate. For example, sometechniques do not identify certain types of impairments. For anotherexample, some techniques do not prioritize the repair of impairmentsbased on the severity of the impairments and/or the number of affectedcustomers.

In accordance with some embodiments, a method for noise analysis in anetwork is described. The method comprises: determining, for a firstplurality of devices on the network, upstream SNR (and/or CER) valuesfor a plurality of upstream channels; identifying a noisy upstreamchannel based on whether channels of the plurality of upstream channelsmeet a noisy channel criteria; and identifying a plurality of suspectdevices based on respective devices of the plurality of devices meetinga set of one or more suspect criteria, wherein the plurality of suspectdevices is less than the first plurality of devices, and wherein the setof one or more suspect criteria includes a channel criterion that is metfor a respective device when the respective device has communicated onthe noisy upstream channel.

In accordance with some embodiments, a (optionally non-transitory)computer-readable storage medium is described. The computer-readablestorage medium stores one or more programs for noise analysis in anetwork, the one or more programs configured to be executed by one ormore processors of an electronic device, and the one or more programsincluding instructions for: determining, for a first plurality ofdevices on the network, upstream SNR (and/or CER) values for a pluralityof upstream channels; identifying a noisy upstream channel based onwhether channels of the plurality of upstream channels meet a noisychannel criteria; and identifying a plurality of suspect devices basedon respective devices of the plurality of devices meeting a set of oneor more suspect criteria, wherein the plurality of suspect devices isless than the first plurality of devices, and wherein the set of one ormore suspect criteria includes a channel criterion that is met for arespective device when the respective device has communicated on thenoisy upstream channel.

In accordance with some embodiments, an electronic device is described.The electronic device includes: one or more processors; and memorystoring one or more programs for noise analysis in a network, the one ormore programs configured to be executed by the one or more processors,and the one or more programs including instructions for determining, fora first plurality of devices on the network, upstream SNR (and/or CER)values for a plurality of upstream channels; identifying a noisyupstream channel based on whether channels of the plurality of upstreamchannels meet a noisy channel criteria; and identifying a plurality ofsuspect devices based on respective devices of the plurality of devicesmeeting a set of one or more suspect criteria, wherein the plurality ofsuspect devices is less than the first plurality of devices, and whereinthe set of one or more suspect criteria includes a channel criterionthat is met for a respective device when the respective device hascommunicated on the noisy upstream channel.

In accordance with some embodiments, a method for noise localization ina network is described. The method includes: identifying one or morechannels that are affected by upstream noise on the network; identifyinga plurality of devices on the network that are attached to the one ormore channels that are affected by upstream noise; displaying a map;subsequent to identifying the one or more channels that are affected byupstream noise: determining, for at least two devices of the pluralityof devices that are attached to the one or more channels, respectivenoise scores; and subsequent to determining the respective noise scores,displaying, on the map, visual indications of the at least two devicesof the plurality of devices that are attached to the one or morechannels, wherein: in accordance with a determination that thedetermined noise score of a respective device is within a first noisescore range, the visual indication of the respective device has a firstcharacteristic without having a second characteristic; and in accordancewith a determination that the determined noise score of the respectivedevice is within a second noise score range, the visual indication ofthe respective device has the second characteristic without having thefirst characteristic.

In accordance with some embodiments, a (optionally non-transitory)computer-readable storage medium is described. The computer-readablestorage medium stores one or more programs for noise localization in anetwork, the one or more programs configured to be executed by one ormore processors of an electronic device with a display, and the one ormore programs including instructions for: identifying one or morechannels that are affected by upstream noise on the network; identifyinga plurality of devices on the network that are attached to the one ormore channels that are affected by upstream noise; displaying a map;subsequent to identifying the one or more channels that are affected byupstream noise: determining, for at least two devices of the pluralityof devices that are attached to the one or more channels, respectivenoise scores; and subsequent to determining the respective noise scores,displaying, on the map, visual indications of the at least two devicesof the plurality of devices that are attached to the one or morechannels, wherein: in accordance with a determination that thedetermined noise score of a respective device is within a first noisescore range, the visual indication of the respective device has a firstcharacteristic without having a second characteristic; and in accordancewith a determination that the determined noise score of the respectivedevice is within a second noise score range, the visual indication ofthe respective device has the second characteristic without having thefirst characteristic.

In accordance with some embodiments, an electronic device is described.The electronic device includes: a display; one or more processors; andmemory storing one or more programs for noise localization in a network,the one or more programs configured to be executed by the one or moreprocessors, and the one or more programs including instructions for:identifying one or more channels that are affected by upstream noise onthe network; identifying a plurality of devices on the network that areattached to the one or more channels that are affected by upstreamnoise; displaying a map; subsequent to identifying the one or morechannels that are affected by upstream noise: determining, for at leasttwo devices of the plurality of devices that are attached to the one ormore channels, respective noise scores; and subsequent to determiningthe respective noise scores, displaying, on the map, visual indicationsof the at least two devices of the plurality of devices that areattached to the one or more channels, wherein: in accordance with adetermination that the determined noise score of a respective device iswithin a first noise score range, the visual indication of therespective device has a first characteristic without having a secondcharacteristic; and in accordance with a determination that thedetermined noise score of the respective device is within a second noisescore range, the visual indication of the respective device has thesecond characteristic without having the first characteristic.

In accordance with some embodiments, a method for analyzing a network isdescribed. The method includes: concurrently displaying: a graphicalrepresentation of a network quality metric graphed against a firstduration of time for a signal; and a map of an area, wherein the mapincludes concurrent display of: one or more geographical elements of thearea that are not network devices, and a plurality of network devices;while displaying the graphical representation of the network qualitymetric for the signal, receiving input selecting a first time that iswithin the first duration of time; and in response to receiving theinput selecting the first time, updating the map of the area to change avisual characteristic of at least some of the displayed plurality ofnetwork devices based on a respective noise score for the correspondingnetwork devices at the selected first time.

In accordance with some embodiments, a device for analyzing a network isdescribed. The device comprises one or more process; and memory storingone or more programs configured to be executed by the one or moreprocessors, the one or more programs including instructions for:concurrently displaying: a graphical representation of a network qualitymetric graphed against a first duration of time for a signal; and a mapof an area, wherein the map includes concurrent display of: one or moregeographical elements of the area that are not network devices, and aplurality of network devices; while displaying the graphicalrepresentation of the network quality metric for the signal, receivinginput selecting a first time that is within the first duration of time;and in response to receiving the input selecting the first time,updating the map of the area to change a visual characteristic of atleast some of the displayed plurality of network devices based on arespective noise score for the corresponding network devices at theselected first time.

In accordance with some embodiments, a computer-readable storage mediumfor analyzing a network is described. The computer-readable storagemedium includes one or more programs configured to be executed by one ormore processors, the one or more programs including instructions for:concurrently displaying: a graphical representation of a network qualitymetric graphed against a first duration of time for a signal; and a mapof an area, wherein the map includes concurrent display of: one or moregeographical elements of the area that are not network devices, and aplurality of network devices; while displaying the graphicalrepresentation of the network quality metric for the signal, receivinginput selecting a first time that is within the first duration of time;and in response to receiving the input selecting the first time,updating the map of the area to change a visual characteristic of atleast some of the displayed plurality of network devices based on arespective noise score for the corresponding network devices at theselected first time.

In accordance with some embodiments, a method for analyzing a network isdescribed. The method includes: concurrently displaying: a graphicalrepresentation of a network quality metric graphed against a firstduration of time for a signal; and a map of an area, wherein the mapincludes concurrent display of: one or more geographical elements of thearea that are not network devices, and a plurality of network devices;while displaying the graphical representation of the network qualitymetric for the signals, receiving first input selecting a first timethat is within the first duration of time; and in response to receivingthe first input, displaying a first visual indicator corresponding tothe first time in the graphical representation of the network qualitymetric; while displaying the graphical representation of the networkquality metric for the signals, receiving second input selecting asecond time, different from the first time, that is within the firstduration of time; and in response to receiving the second input,displaying a second visual indicator corresponding to the second time inthe graphical representation of the network quality metric; subsequentto receiving the first input and the second input: determining a changein a noise score for each of the plurality of network devices betweenthe first time and the second time; determining whether the respectivechange in the noise score for each respective network device of theplurality of network devices meets a noise score change criteria;displaying, based on the determinations of whether respective changes inthe noise scores meet the noise score change criteria, the map of thearea such that: respective network devices of the plurality of networkdevices that meet the noise score change criteria are displayed using afirst visual appearance.

In accordance with some embodiments, a device for analyzing a network isdescribed. The device includes one or more processors; and memorystoring one or more programs configured to be executed by the one ormore processors, the one or more programs including instructions for:concurrently displaying: a graphical representation of a network qualitymetric graphed against a first duration of time for a signal, and a mapof an area, wherein the map includes concurrent display of: one or moregeographical elements of the area that are not network devices, and aplurality of network devices; while displaying the graphicalrepresentation of the network quality metric for the signals, receivingfirst input selecting a first time that is within the first duration oftime; and in response to receiving the first input, displaying a firstvisual indicator corresponding to the first time in the graphicalrepresentation of the network quality metric; while displaying thegraphical representation of the network quality metric for the signals,receiving second input selecting a second time, different from the firsttime, that is within the first duration of time; and in response toreceiving the second input, displaying a second visual indicatorcorresponding to the second time in the graphical representation of thenetwork quality metric; subsequent to receiving the first input and thesecond input: determining a change in a noise score for each of theplurality of network devices between the first time and the second time;determining whether the respective change in the noise score for eachrespective network device of the plurality of network devices meets anoise score change criteria; displaying, based on the determinations ofwhether respective changes in the noise scores meet the noise scorechange criteria, the map of the area such that: respective networkdevices of the plurality of network devices that meet the noise scorechange criteria are displayed using a first visual appearance.

In accordance with some embodiments, a computer-readable storage mediumfor analyzing a network is described. The computer-readable storagemedium store one or more programs configured to be executed by one ormore processors, the one or more programs including instructions for:concurrently displaying: a graphical representation of a network qualitymetric graphed against a first duration of time for a signal; and a mapof an area, wherein the map includes concurrent display of: one or moregeographical elements of the area that are not network devices, and aplurality of network devices; while displaying the graphicalrepresentation of the network quality metric for the signals, receivingfirst input selecting a first time that is within the first duration oftime; and in response to receiving the first input, displaying a firstvisual indicator corresponding to the first time in the graphicalrepresentation of the network quality metric; while displaying thegraphical representation of the network quality metric for the signals,receiving second input selecting a second time, different from the firsttime, that is within the first duration of time; and in response toreceiving the second input, displaying a second visual indicatorcorresponding to the second time in the graphical representation of thenetwork quality metric; subsequent to receiving the first input and thesecond input: determining a change in a noise score for each of theplurality of network devices between the first time and the second time;determining whether the respective change in the noise score for eachrespective network device of the plurality of network devices meets anoise score change criteria; displaying, based on the determinations ofwhether respective changes in the noise scores meet the noise scorechange criteria, the map of the area such that: respective networkdevices of the plurality of network devices that meet the noise scorechange criteria are displayed using a first visual appearance.

In accordance with some embodiments, a method for analyzing a network isdescribed. The method comprises: determining a first time at which afirst type of network impairment is negatively affecting the network;determining a second time at which the first type of network impairmentis not negatively affecting the network or is negatively affecting thenetwork less than at the first time; calculating, for each of aplurality of network devices of the network: a first noise score for thefirst time using a first calculation; a second noise score for the firsttime using a second calculation different from the first calculation; athird noise score for the second time using the first calculation; and afourth noise score for the second time using the second calculation;determining, for each of the plurality of network devices: a firstdifference score by calculating a difference between the first noisescore and the third noise score for the respective network device; and asecond difference score by calculating a difference between the secondnoise score and the fourth noise score for the respective networkdevice; identifying the first calculation as an indicator of the firsttype of network impairment when a subset of the plurality of networkdevices have first difference scores that exceed a threshold difference;and identifying the second calculation as an indicator of the first typeof network impairment when a subset of the plurality of network deviceshave second difference scores that exceed the threshold difference.

In accordance with some embodiments, a device for analyzing a network isdescribed. The device includes one or more processors; and memorystoring one or more programs configured to be executed by the one ormore processors, the one or more programs including instructions for:determining a first time at which a first type of network impairment isnegatively affecting the network: determining a second time at which thefirst type of network impairment is not negatively affecting the networkor is negatively affecting the network less than at the first time;calculating, for each of a plurality of network devices of the network:a first noise score for the first time using a first calculation; asecond noise score for the first time using a second calculationdifferent from the first calculation; a third noise score for the secondtime using the first calculation; and a fourth noise score for thesecond time using the second calculation; determining, for each of theplurality of network devices: a first difference score by calculating adifference between the first noise score and the third noise score forthe respective network device; and a second difference score bycalculating a difference between the second noise score and the fourthnoise score for the respective network device; identifying the firstcalculation as an indicator of the first type of network impairment whena subset of the plurality of network devices have first differencescores that exceed a threshold difference; and identifying the secondcalculation as an indicator of the first type of network impairment whena subset of the plurality of network devices have second differencescores that exceed the threshold difference.

In accordance with some embodiments, a computer-readable storage mediumfor analyzing a network is described. The computer-readable storagemedium store one or more programs configured to be executed by one ormore processors, the one or more programs including instructions for:determining a first time at which a first type of network impairment isnegatively affecting the network; determining a second time at which thefirst type of network impairment is not negatively affecting the networkor is negatively affecting the network less than at the first time;calculating, for each of a plurality of network devices of the network:a first noise score for the first time using a first calculation; asecond noise score for the first time using a second calculationdifferent from the first calculation; a third noise score for the secondtime using the first calculation; and a fourth noise score for thesecond time using the second calculation; determining, for each of theplurality of network devices: a first difference score by calculating adifference between the first noise score and the third noise score forthe respective network device; and a second difference score bycalculating a difference between the second noise score and the fourthnoise score for the respective network device; identifying the firstcalculation as an indicator of the first type of network impairment whena subset of the plurality of network devices have first differencescores that exceed a threshold difference; and identifying the secondcalculation as an indicator of the first type of network impairment whena subset of the plurality of network devices have second differencescores that exceed the threshold difference.

DESCRIPTION OF THE FIGURES

For a better understanding of the various described embodiments,reference should be made to the Description of Embodiments below, inconjunction with the following drawings in which like reference numeralsrefer to corresponding parts throughout the figures.

FIG. 1 illustrates an exemplary electronic device, in accordance withsome embodiments.

FIGS. 2A-2C illustrates an exemplary flow diagram, in accordance withsome embodiments.

FIG. 3 illustrates a portion of an exemplary cable network map, inaccordance with some embodiments.

FIG. 4 illustrates an exemplary map, in accordance with someembodiments.

FIG. 5 illustrates an exemplary flow diagram, in accordance with someembodiments.

FIGS. 6A-6D illustrate exemplary user interfaces for analyzing anetwork, in accordance with some embodiments.

FIG. 7 illustrates an exemplary flow diagram, in accordance with someembodiments.

FIGS. 8A-8D illustrate exemplary user interfaces for analyzing anetwork, in accordance with some embodiments.

FIGS. 9A-9B illustrates an exemplary flow diagram, in accordance withsome embodiments.

DESCRIPTION OF EMBODIMENTS

The following description sets forth exemplary methods, parameters, andthe like. It should be recognized, however, that such description is notintended as a limitation on the scope of the present disclosure, but isinstead provided as a description of exemplary embodiments.

One significant impairment in a cable network is upstream noise.Upstream noise can enter from one or more points of impairments into thenetwork (e.g., ingress) or be generated from a network device (or at aninterconnection of a network device) such as a cable modem within thenetwork. Upstream noise on one upstream data communication channeltravels upward toward the fiber-node or cable modem termination system(CMTS) and impairs the communication of all devices on the same upstreamchannel. However, it is difficult to detect (or to accurately detect)(1) from where the noise entered into the network and/or (2) whichnetwork device generated the noise. In some examples, the location ofthe source of upstream noise can be found by physically disconnectingdifferent legs of the network or different devices and confirmingwhether the noise continues to exist or is gone. This process ofelimination can be very time consuming, costly, and labor intensive. Inadditional, this process of elimination can be impractical, such as whenthe noise source is located at a customer premise or the noise is comingfrom customer premise equipment (CPE). In some examples, a spectrumanalyzer or spectrum analyzer function (e.g., of a cable modem and/orCMTS) can be used to attempt to detect and localize noise. However, suchtechniques are cumbersome and inefficient. Importantly, spectrumanalyzer-based techniques are often inaccurate in detecting andlocalizing the source of upstream noise.

In networks, such as in a cable network (e.g., a DOCSIS network), asignificant amount of data related to the functioning of network devices(e.g., a cable modem (CM) or modem, a cable modem termination system(CMTS)) is collected and, optionally, transmitted across the network.These network parameters include, but are not limited to, upstream anddownstream (transmit and received) power levels, upstream and downstreamsignal to noise ratio (SNR), Codeword Error (CER), and pre-equalizationand post equalization parameters. These parameters are used formonitoring the performance of the network as well as performing reactiveand proactive network maintenance (PNM). In the presence of upstreamnoise, some of these parameter values change differently in differentnetwork segments relative to the location where noise is entering (orbeing generated in) the network (noise location), and therefore, can beused to determine the location of the noise.

The values of many of these network parameters show different levels ofinconsistency in time and for different channel frequencies, in thepresence of noise and for different types of noise and in differentnetwork segments. Different network impairments result in differentinconsistencies in the network parameters. For example, theinconsistencies differ when other types of (or additional) impairmentsare present in the network. One particular challenge is that multiple(or all) modems affected by the same noise on upstream can show the samekind of inconsistencies, thereby making it more difficult to localizethe source of the noise. Using advanced statistical analysis and/orartificial intelligence techniques, it is possible to analyze theinconsistencies (in time, in frequency, for noise type and for networksegment, etc.) of the values of network parameters, their combinationand/or correlation identify the source of noise and/or to locate where(or near to what network node) noise is entering the network. In someexamples, this process produces one or more noise scores showing whichmodem is likely to be the source of noise. In some examples, thisprocess produces one or more noise scores showing where (e.g., at whichnetwork node, near which network node) noise is likely entering thenetwork. In some examples, this process produces one or more noisescores showing what type of noise is likely present in the network. Someexamples of the types of noise that can be identified include:

1. Noise that entered the network through an impairment in the network,such as a damaged cable or a damaged amplified connector (ingress);

2. Noise that was generated/emitted by a CPE (e.g., cable modem or a settop box);

3. Noise that was generated by a CPE that was cause by an impairment inthe network. For example, a loose connected near/at the CPE may impairthe grounding of the shield and cause the CPE to emit noise;

4. Noise generated by CPEs (Docsis modems or Set Top Boxes) due tohigher than allowed upstream transmit power:

5. Noise generated in the network by an impairment, such as Common PathDistortion (CPD) from, for example, a corroded and/or loose connector;and

6. Noise generated by amplifiers due to higher than allowed upstreamamplification gain.

The process monitors parameters of the network to identify the type ofnoise and/or to localize noise. Noise can be localized by monitoring theparameters over time and/or over multiple channel frequencies. Thevalues of the parameters also reflect the type of noise and/or theproximity to the noise source. Noise localization is performed using oneor more of the parameters to generate noise scores for respectivenetwork devices. In some examples, one or more first order derivatives(rate of change; in time) of the parameters are also used to generatethe noise score. In some examples, one or more second order derivatives(how the rate of change is changing, in time) of the parameters are usedto generate the noise score. The parameters optionally used to generatenoise scores can be categorized into one of several categories,including (1) parameters obtained directly from the modems and/orCMTSes, (2) parameters calculated using category 1 parameter values(e.g., parameters obtained directly from modems/CMTSes), (3) parametersobtained by analyzing variations of the category 1 and 2 parameters overtime (e.g., over a single channel), (4) parameters obtained by analyzingvariations of parameters of category 1 and 2 over multiple channelfrequencies (e.g., at a single point in time), (5) parameters obtainedfrom combining the parameters in categories 1, 2, 3 and 4, and (6)calculated parameters that show dependencies and/or correlation betweenthe parameters in any two or more of categories 1, 2, 3, 4 and 5.

Exemplary category 1 parameters obtained directly from modems and/orCMTSes optionally include: CM Upstream signal to noise ratio (SNR), CMTransmit power level, CM Downstream SNR, CM Downstream Power level, CMCodeword Error Rate, CM Pre-Equalization coefficients, CMTS interfacesignal to noise ratio, CMTS receive power level, CMTS Codeword ErrorRate, and CMTS Post-Equalization coefficients.

Exemplary category 2 parameters calculated using category 1 parametervalues optionally include: CM Micro Reflection Level, CM FrequencyResponse, CM Group Delay, CMTS Micro Reflection, CMTS FrequencyResponse. CMTS Group Delay. NMTER—Non Main Tap Energy to Total TapEnergy, CMTS NMTER—CMTS Non Main Tap Energy to Total Tap Energy,MTR—Main Tap Ratio, TTE—Total Tap Energy, Downstream Noise SpectralDensity, Upstream Noise Spectral Density.

Exemplary category 3 parameters obtained by analyzing variations of thecategory 1 and 2 parameters over time optionally include: standarddeviation of category 1 and 2 parameters, coefficient of variation ofcategory 1 and 2 parameters, first order derivative (rate of change intime) of 1 and 2 parameters, and second order derivative (how the rateof change is changing in time) of 1 and 2 parameters.

Exemplary category 4 parameters obtained by analyzing variations of thecategory 1 and 2 parameters over channel frequencies optionally include:standard deviation of category 1 and 2 parameters, coefficient ofvariation of category 1 and 2 parameters, first order derivative (rateof change in time) of 1 and 2 parameters, and second order derivative(how the rate of change is changing in time) of 1 and 2 parameters.

Exemplary category 5 parameters obtained by analyzing combination of thecategory 1, 2 and 3 parameters over time optionally include: NMTER toSNR ratio, Transmit power level to SNR ratio, etc.

Exemplary category 6 parameters obtained by analyzing correlation of thecategory 1, 2 and 3 parameters over time optionally include: Transmitpower level variation and NMTER variation correlation in time, Transmitpower level variation and MTER variation correlation in time, Transmitpower level variation and TTE variation correlation in time, etc.

FIG. 1 illustrates an exemplary electronic device (e.g., a server, acomputer), in accordance with some embodiments. In some examples, thetechniques described below can be performed at device 100. Device 100 isan electronic device with one or more processors 102, one or moredisplays 104, one or more memories 106, one or more network interfacecards 110, one or more input devices (e.g., keyboard 112), one or moreoutput device 114 (e.g., printer), connected via one or morecommunication buses 108. Many of elements of device 100 are optional,such as display 104, input devices 112, and output devices 114. Memories106 can include random access memory, read-only memory, flash memory,and the like. Memory 106 can include a non-transitory computer-readablestorage medium. The non-transitory computer-readable storage medium isconfigured to store one or more programs configured to be executed bythe one or more processors 102 of device 100. The one or more programsoptionally include instructions for performing the described techniques.

FIGS. 2A-2C illustrate an exemplary flow diagram for noise analysis in anetwork, in accordance with some embodiments. In some examples, thetechnique is performed at an electronic device (e.g., device 100), suchas on an analysis server or computer optionally connected to thenetwork. In some examples, the electronic device includes memory storingone or more programs for execution by one or more processors of theelectronic device, the one or more programs including instructions forperforming the technique.

The technique checks the upstream SNR (and/or CER) for multiple deviceson multiple channels to identify a noisy upstream channel. Once a noiseupstream channel is identified, the technique identifies correspondingsuspect devices communicating on the noisy channel. In some example, thesuspect devices are devices on the network that may potentially beintroducing excess noise into the network.

At block 202, the analysis server (or computer) determines (e.g., pollsfor and receives), for a first plurality of devices (e.g., a pluralityof CMs) on the network, upstream SNR values for a plurality of upstreamchannels (e.g., for a duration of time). In some examples, the analysisserver monitors communications on the network and determines (e.g.,polls for, receives from one or more CMTSs) SNR values for multipledevices (e.g., each device on the network) for multiple upstreamchannels (e.g., for each upstream channel of the network, for apredetermined set of upstream channels) on which each device transmits.In some examples, determining the first plurality of devices isindependent of downstream SNR values for the devices.

At block 204, the analysis server identifies a noisy upstream channel(or, optionally, a plurality of noisy upstream channels) based onwhether channels of the plurality of upstream channels meet a noisychannel criteria (e.g., for the duration of time). In some examples, thenoisy channel criteria are met for channels that have an upstream SNRbelow a threshold. For example, the technique detects (e.g., measures,determines) SNR values for multiple devices on a particular upstreamchannel. In some examples, the SNR for the particular upstream channelis an average SNR for that channel. The technique determines whether theSNR for the particular channel is below the threshold. When the SNR forthe particular channel is below the threshold, the particular channel isidentified as being a noisy upstream channel. When the SNR for theparticular channel is not below the threshold, the particular channel isnot identified as being a noisy upstream channel. In some examples, thethreshold is 30 dB. Thus, upstream channels with SNR below 30 dB areidentified as noisy upstream channels. In some examples, the noisyupstream channel is identified based on having the lowest SNR of theplurality of upstream channels.

At block 206, the analysis server identifies a plurality of suspectdevices (or a single suspect device) based on respective devices of theplurality of devices meeting (e.g., for the duration of time) a set ofone or more suspect criteria (e.g., if a particular device of theplurality of devices meets the set of suspect criteria, that particulardevice is identified as being in the suspect plurality of devices). Theplurality of suspect devices is (a subset) less than the first pluralityof devices.

At block 208, the set of one or more suspect criteria includes a channelcriterion that is met for a respective device when the respective devicehas communicated (e.g., during the duration of time) on the noisyupstream channel (e.g., during the time used to determine the upstreamSNR for the noise upstream channel). In some examples, the analysisserver monitors communications on the network and detects that certaindevices (e.g., CM) transmit on the noisy upstream channel. In someexamples, the analysis server compiles a list of devices communicatingon the noisy upstream channel during a time period that the noisychannel criteria was met. For example, the plurality of suspect devicesare identified as potentially (or likely) containing the source of thenoise or being affected by noise present in the network, such as ingressnoise or noise generated by other modems.

At block 210, the technique optionally reduces the number of suspectdevices based on the pre-equalizer coefficients of devices. For example,the set of one or more suspect criteria optionally includes apre-equalizer criterion that is met for the respective device based on apre-equalizer coefficient of the respective device (e.g., for the noisychannel). In some examples where the set of one or more suspect criteriaincludes the pre-equalizer criterion, the technique analyzespre-equalizer coefficients of the second plurality of devices on thenetwork to determine whether the devices meet the pre-equalizercriterion. If a device meets the pre-equalizer criterion and the othercriterions of the set of one or more suspect criteria, the device isidentified as a suspect device. If the device does not meet thepre-equalizer criterion, the device is not identified as a suspectdevice. For example, some devices use pre-equalization to modifycarriers (e.g., pre-distort carriers) to (partially or fully) offset orcancel out distortions caused by reflections (e.g., micro-reflections)in the network. The pre-equalizer coefficient of the device is anindication of whether and to what degree the device is compensating forparticular problems in the network. In some examples, pre-equalizercriterion that is met for the respective device when the pre-equalizercoefficient of the respective device is a non-zero value. In someexamples, pre-equalizer criterion that is met for the respective devicewhen the pre-equalizer coefficient of the respective device is with apredetermined range of values. In some examples, pre-equalizer criterionthat is met for the respective device when the pre-equalizer coefficientof the respective device exceeds (or alternatively, does not exceed) adetermined coefficient value. In some examples, pre-equalizer criterionis variable and the technique adjusts the pre-equalizer criterion toreduce or increase the number of identified suspect devices. Forexample, if no suspect devices are initially identified using an initialpre-equalizer criterion, the technique automatically adjusts thecriterion to increase the number of suspect devices.

At block 212, the technique optionally reduces the number of suspectdevices based on the respective device's upstream transmit power levels.In some examples, the set of one or more suspect criteria optionallyincludes an upstream transmit power level criterion that is met for therespective device when a determined (e.g., measured, received) upstreamtransmit power level value for the respective device (e.g., for thenoisy channel) exceeds an (e.g., non-zero) upstream transmit power levelthreshold. In some examples, the upstream transmit power level is (or isbased on) the power with which the respective device is transmitting ona suspect channel (e.g., over the duration of time).

At block 214, the technique optionally reduces the number of suspectdevices based on the respective device's upstream transmit power levelstandard deviation. In some examples, the set of one or more suspectcriteria optionally includes an upstream transmit power level standarddeviation criterion that is met for the respective device when adetermined (e.g., measured, received) upstream transmit power levelstandard deviation value for the respective device (e.g., for the noisychannel) exceeds an (e.g., non-zero) upstream transmit power levelstandard deviation threshold. In some examples, the upstream transmitpower level standard deviation is (or is based on) the standarddeviation of the power with which the respective device is transmittingon a suspect channel (e.g., over the duration of time). Generally, thehigher the standard deviation of the upstream transmit power level, themore likely the device is affect by noise.

At block 216, the technique optionally reduces the number of suspectdevices based on the respective device's NMTER. In some examples, theset of one or more suspect criteria optionally includes an NMTER(non-main tap energy to total tap energy ratio) criterion that is metfor the respective device when a determined (e.g., measured, received)NMTER value for the respective device (e.g., for the noisy channel)exceeds an (e.g., non-zero) NMTER threshold. In some examples, thenon-main tap energy (or its standard deviation) is used as a criteria inthe set of one or more suspect criteria.

At block 218, the technique optionally reduces the number of suspectdevices based on the respective device's NMTER standard deviation. Insome examples, the set of one or more suspect criteria optionallyincludes an NMTER (non-main tap energy to total tap energy ratio)standard deviation criterion that is met for the respective device whena determined (e.g., measured, received) NMTER standard deviation valuefor the respective device (e.g., for the noisy channel) exceeds an(e.g., non-zero) NMTER standard deviation threshold. Generally, thehigher the standard deviation of the NMTER, the more likely the deviceis affect by noise.

At block 220, the technique optionally reduces the number of suspectdevices based on the respective device's power level-to-NMTERcorrelation. In some examples, the set of one or more suspect criteriaoptionally includes a (upstream) power level-to-NMTER (non-main tapenergy to total tap energy ratio) correlation criterion that is met forthe respective device when a determined (e.g., measured, received) powerlevel-to-NMTER correlation value for the respective device exceeds a(e.g., non-zero) power level-to-NMTER correlation threshold. Forexample, the technique determines the correlation between the upstreamtransmit power level of the respective device and the NMTER of therespective device. In some examples, the correlation is measured as anumber of points (e.g., in percent) where NMTER metric value changesaccording to power level change, such that the percent value indicatesthe probability (or likelihood) that the device raises the power levelto overcome the noise floor. Generally, the higher the correlation, themore likely the respective device is affected by the noise. In someexamples, this percent value is used as a noise score for the respectivedevice.

At block 222, the technique optionally reduces the number of suspectdevices based on the respective device's NMITE. In some examples, theset of one or more suspect criteria optionally includes an NMITE(non-main tap individual tap energy) criterion that is met for therespective device when a determined (e.g., measured, received) NMITEvalue for the respective device (e.g., for the noisy channel) exceeds(or does not exceed) an (e.g., non-zero) NMITE threshold.

At block 224, the technique optionally reduces the number of suspectdevices based on the respective device's SNR. In some examples, the setof one or more suspect criteria includes an (upstream) SNR(signal-to-noise ratio) criterion that is met for the respective devicewhen a determined (e.g., measured, received) SNR value for therespective device (e.g., for the noisy channel) does not exceed an(e.g., non-zero) SNR threshold. Generally, the lower the SNR, the morelikely the device is affect by the noise. In some examples, thisanalysis is performed for each of the plurality of upstream channels.

At block 224, the technique optionally reduces the number of suspectdevices based on the respective device's SNR standard deviation. In someexamples, the set of one or more suspect criteria optionally includes an(upstream) SNR (signal-to-noise ratio) standard deviation criterion thatis met for the respective device when a determined (e.g., measured,received, calculated) SNR standard deviation value for the respectivedevice (e.g., for the noisy channel) exceeds an (e.g., non-zero) SNRstandard deviation threshold. Generally, the higher the SNR standarddeviation value, the more likely the device is affect by the noise. Insome examples, this analysis is performed for each of the plurality ofupstream channels.

At block 224, the technique optionally reduces the number of suspectdevices based on the respective device's CER. In some examples, the setof one or more suspect criteria optionally includes a (upstream) CER(codeword error rate) criterion that is met for the respective devicewhen a determined (e.g., measured, received) CER value for therespective device (e.g., for the noisy channel) exceeds a (e.g.,non-zero) CER threshold. Generally, the higher the CER value, the morelikely the device is affect by the noise. In some examples, the initialCER threshold is 1*10{circumflex over ( )}−9.

In some embodiments, the technique optionally reduces the number ofsuspect devices based on one or more parameters selected from one ormore of the above-described categories: (1) parameters obtained directlyfrom the modems and/or CMTSes, (2) parameters calculated using category1 parameter values (e.g., parameters obtained directly frommodems/CMTSes), (3) parameters obtained by analyzing variations of thecategory 1 and 2 parameters over time (e.g., over a single channel), (4)parameters obtained by analyzing variations of parameters of category 1and 2 over multiple channel frequencies (e.g., at a single point intime), (5) parameters obtained from combining the parameters incategories 1, 2, 3 and 4, and (6) calculated parameters that showdependencies and/or correlation between the parameters in any two ormore of categories 1, 2, 3, 4 and 5.

At block 230, the technique optionally reduces (or increases) the numberof suspect devices by changing one or more thresholds. In some examples,the analysis server determines (after initially determining theplurality of suspect devices) whether the quantity of devices of theplurality of suspect devices meets a quantity metric (e.g., not enoughsuspect devices identified, too many suspect devices identified).

At block 232, in accordance with a determination that the quantity ofdevices of the plurality of suspect devices does not meet the quantitymetric, the analysis server adjusts (e.g., based on the quantity ofdevices of the plurality of suspect devices) one or more of: theupstream transmit power level threshold, the upstream transmit powerlevel standard deviation threshold, the NMTER threshold, the NMTERstandard deviation threshold, the NMITE threshold, SNR threshold, SNRstandard deviation threshold, and CER threshold.

At block 234, subsequent to the adjusting at block 232, the analysisserver updates the identified plurality of suspect devices (or a singlesuspect device) based on respective devices of the plurality of devicesmeeting (e.g., for the duration of time) the set of one or more suspectcriteria with at least one adjusted threshold value (or a plurality ofadjusted threshold values). In some examples, in accordance with adetermination that the quantity of devices of the plurality of suspectdevices does meet the quantity metric, the technique forgoes adjusting(any of) the thresholds and forgoes updating identified plurality ofsuspect devices.

At block 238, the technique optionally determines (e.g., by calculating)noise scores for (at least some of, each of) the plurality of suspectdevices, the noise scores indicating the likelihood of the devicescausing noise above a noise threshold and/or the likelihood of thedevices being in proximity of a point of entry of noise into thenetwork.

At block 240, the noise scores are optionally calculated using weightsand the number of conditions (e.g., of blocks 210-238) that are met. Insome examples, the analysis server determining (e.g., by calculating)noise scores for (at least some of, each of) the plurality of suspectdevices includes assigning weights to a plurality of network parametersfor the plurality of suspect devices, and calculating weight-adjustednoise scores for (at least some of, each of) the plurality of suspectdevices, the weight-adjusted noise scores indicating the likelihood ofthe devices causing noise above a noise threshold and/or the likelihoodof the devices being in proximity of a point of entry of noise into thenetwork (and, optionally, the type of noise). In some examples, theweight of a respective network parameter is the same for all devices inthe plurality of suspect devices even when the plurality of suspectdevices includes a various brands, types, capabilities, etc. In someexamples, the weight of a respective network parameter varies forvarious devices in the plurality of suspect devices based on one or moreof: a brand of the device, a model of the device, a hardware or softwareversion of the device, a type of the device, and capabilities of thedevice. For example, the weighting of the NMTER standard deviation maybe set to a weighting (e.g., 2.5) that is different from the weightingof the NMITE (e.g., 12), that is different from the weighting of theupstream transmit power level (e.g., 20).

In some examples, rather than (or in addition to) reducing the devicesidentified as suspect devices by including additional criteria into theset of one or more suspect criteria, noise scores are calculated for thedevices identified as having communicated (e.g., during the duration oftime) on the noisy upstream channel. The noise scores are calculatedbased on whether they meet one or more of the upstream transmit powerlevel threshold, the NMTER threshold, the NMTER standard deviationthreshold, the NMITE threshold, SNR threshold, SNR standard deviationthreshold, and CER threshold. Devices that meet more of the followingcriterions are assigned higher noise scores: pre-equalizer criterion,upstream transmit power level criterion, upstream transmit power levelstandard deviation criterion, NMTER (non-main tap energy to total tapenergy ratio) criterion, NMTER (non-main tap energy to total tap energyratio) standard deviation criterion, power level-to-NMTER (non-main tapenergy to total tap energy ratio) correlation criterion, NMITE (non-maintap individual tap energy) criterion. SNR (signal-to-noise ratio)criterion, SNR (signal-to-noise ratio) standard deviation criterion, CER(codeword error rate) criterion, with certain criterions being assignedweights other than 1 w % bile other criterions are assigned a weight of1.

At block 242, the noise scores are optionally calculated using alogistic regression model. In some examples, the analysis serverdetermining (e.g., by calculating) noise scores for (at least some of,each of) the plurality of suspect devices includes providing a pluralityof network parameters for the plurality of suspect devices to a logisticregression model to calculate the noise scores for the plurality ofdevices (and, optionally, the type of noise).

At block 244, the noise scores are optionally calculated using a neuralnetwork machine learning model. In some examples, the analysis serverdetermining (e.g., by calculating) noise scores for (at least some of,each of) the plurality of suspect devices includes providing a pluralityof network parameters for the plurality of suspect devices to a neuralnetwork machine learning model to calculate the noise scores for theplurality of devices (and, optionally, the type of noise).

In accordance with some examples, the plurality of network parameters(of the devices) are selected from among one or more of: (a) CodewordError Rate, (b) Micro Reflection Level, (c) CM Pre-Equalized FrequencyResponse, (d) CMTS CM Equalized Frequency Response, (e) Main Tap Ratio,(f) Non Main Tap Energy to Total Tap Energy Ratio, (g) Power Level, (h)Power Level to MTR Ratio, (i) Power Level to NMTER Ratio, (j) PowerLevel to TTE Total Tap Energy Ratio, (k) TTE Total Tap Energy, (l) NonMain Individual Tap Energy, (m) Signal to Noise Ratio, (n) SNR spikelevel above certain threshold over time, and (o) SNR spike count abovecertain threshold over time for a period of time.

At block 246, the technique optionally provides (e.g., displaying on adisplay, transmitting to a remote (display) device) the noise scores. Insome examples, the technique determines (and provides (e.g., displays))the noise scores, which correspond to probabilities that each of thesuspect devices is the cause of significant noise or is in proximity ofthe point of entry of noise into the network and provides theserelationships (probability <-> suspect device relationship) to anotherdevice or to the user. For example, the operator or maintainer of thenetwork can take action to further analyze or correct the devicesidentified with high probability (or the device identified with highestprobability). In some examples, the technique determines (and provides(e.g., displays)) noise scores for one or more types of noise (e.g., foreach of the suspect devices).

As discussed above, a (optionally non-transitory) computer-readablestorage medium optionally stores one or more programs configured to beexecuted by one or more processors of an electronic device (with anoptional display), the one or more programs including instructions forperforming the technique described with respect to FIG. 2A-2C.

As discussed above, an electronic device comprises: (an optionaldisplay), one or more processors, and memory. The memory stores one ormore programs configured to be executed by the one or more processors,the one or more programs including instructions for performing thetechnique described with respect to FIG. 2A-2C.

Another significant impairment in a cable network is white noise, whichtypically affects both upstream frequencies and downstream frequencies.In cable networks, noise generated in or entering a cable network from alocation on the trunk cable, trunk amplifiers, or other passive elementson the distribution network is typically a white noise that affects boththe upstream spectrum and the downstream spectrum. This is particularlytrue when noise enters from one or more points of impairments into thenetwork. For example, the white noise on the cable network can be due toloose connector(s), damaged passive elements(s) (e.g., cable, taps,directional couplers), and/or defective active elements (e.g.,amplifiers, line extenders). However, it can be difficult to determine(or to accurately determine) the location from where the white noiseentered into the network, thereby limiting the ability of the providerto correct the impairment. In some examples, the location of the sourceof white noise can be found by physically disconnecting different legsof the network and confirming whether the noise continues to exist or isgone. This process of elimination can be very time consuming, costly,and labor intensive. In additional this process of elimination can beimpractical, such as when the network is located in a geographicallyremote or difficult to access location.

Typically, the cable network carries the downstream (forward) signalsand upstream (return) signals at different frequencies (e.g., frequencybands that do not overlap). Typically, the lower frequencies carryupstream signals and the higher frequencies carry downstream signals.For example, a network may be designed to carry upstream signals from 5MHz to 45 MHz and to carry downstream signals from 55 MHz to 1 GHz. Insome examples, the network may be designed to allocate a largerfrequency range for upstream to accommodate the desire for higherupstream bandwidth, such as by allocating from 5 MHz to 88 MHz (orhigher) for the upstream signals. White noise has a wide spectrum andtypically covers both (or portions of both) upstream frequencies (lowerfrequencies) and downstream frequencies (higher frequencies).

Network lines in the cable network frequently include amplifiers forboth the downstream signals and the upstream signals. One (or more)downstream amplifier(s) amplify the downstream or forward signals (e.g.,residing at higher frequencies) and one (or more) upstream amplifier(s)amplify the return or upstream signals (e.g., residing at lowerfrequencies). Diplex filters are implemented so as to separate thesignals on the downstream frequencies from the signals at the upstreamfrequencies. In particular, two diplex filters (e.g., one at the inputof the upstream amplifier, one at the input of the downstream amplifier)are used to separate signals at the downstream higher frequencies fromsignals at the upstream lower frequencies and direct them to the rightamplifiers.

As a result of the diplex filters, the higher frequency noise can traveldownstream and affect modems downstream from the location of the noise.However, the higher frequency noise cannot travel upstream (or issignificantly attenuated when travelling upstream) and the noisegenerated in these frequencies do not affect (or have a reduced effect)on modems in the network located upstream (and past a diplex/amplifier)from where the noise is entering the network. Accordingly, only modemswith noisy downstream signals are located downstream from the locationof the noise. In other words, modems that do not have a noisy downstreamare not located downstream from the location of the white noise, thoughthey may be located upstream from the white noise. In contrast, incertain circumstances, modems that are both downstream and upstream fromthe white noise source may have a noisy upstream.

Taps on the network further reduce the ability for higher frequencynoise to travel upstream, thereby limiting the effect of the highfrequency noise on modems in the network located upstream (and past thetap) from where the noise is entering the network. A tap has an input,an output, and one or more legs. Cable modems are optionally connectedto each of the one or more legs. Taps typically have different levels ofattenuation at the input, at the output, and at the legs. For example,transmission between the tap input and the tap leg may incur limitedloss. For another example, transmission between the tap input and thetap output also incurs limited loss. However, transmissions from the tapoutput to the tap leg experience high attenuation; transmissions fromthe tap output to the tap input also experience high attenuation.Accordingly, modems connected upstream (e.g., at the tap legs) from thesource of the white noise will be less affected by the white noise ascompared to modems connected downstream (e.g., to tap legs of tapslocated downstream) from the white noise source.

Because of these characteristics of diplex filters, amplifiers, andtaps, modems located upstream from the white noise source are affecteddifferently from modems located downstream from the same white noisesource. Using this information, the location (e.g., on the network) of anoise source can be determined based on the downstream noise of variousmodems on the network. The downstream noise for modems on the networkcan be determined by polling Downstream SNR for the respective modems,calculating noise Spectral Density (in downstream frequencies) forrespective modems, and/or checking the downstream full band spectrum ofthe respective modems.

FIG. 3 illustrates a portion of an exemplary cable network map 300. Insome embodiments, the technique displays network 300 on a computerdisplay to provide the viewer with noise localization information (e.g.,to identify the area on the map where noise is being introduced into thenetwork). In this example, white noise enters the network at location322. Because taps 310 and 316 are downstream from location 322, cablemodems 312 and 318 connected to the legs of taps 310 and 316,respectively, will experience low downstream SNR as a result of thewhite noise. For example, amplifier 314 will amplify the downstreamsignals (high frequency) along with the high-frequency components of thewhite noise. All devices downstream from the source of the noise (fromlocation 322 to end of the line 320) will experience low SNR

In contrast, tap 306 will attenuate the high-frequency components of thenoise, thereby reducing the amount of noise in the downstream signalthat reaches cable modems 308. Similarly, amplifier 304 (with acorresponding diplex filter) will attenuate the high-frequencycomponents of the noise, thereby reducing the amount of noise in thedownstream signal that reaches cable modems that are connected betweenCMTS 302 and amplifier 304.

In this example, the technique would determine that the upstream channelon which modems 308, 312, and 318 are operating has more noise than athreshold noise amount (e.g., has a lower upstream SNR than a thresholdupstream SNR). As a result, the technique identifies that modems 308,312, and 318 are operating on the one or more channels. The techniqueprobes each of the modems in modems 308, each of the modems in modems312, and each of the modems in modems 318 and receives noise scores,such as downstream signal-to-noise ratios (SNRs), from each of themodems. Because the source of the noise is located at location 322(which is downstream from the location of tap 306), the technique willreceive (in response to the probe) high downstream SNR values from eachof the modems 308. In contrast, the technique will receive (in responseto the probe) low downstream SNR values from each of the modems 312 and318 because the high-frequency component of the white noise willpropagate to those devices without significant attenuation.

FIG. 4 illustrates an exemplary map 400. In some embodiments, thetechnique displays map 400 on a computer display as an alternative todisplay of network map 300. Map 400 can include geographical elements(such as roads and structures) and network elements (such as taps andamps). Map 400 corresponds to the exemplary network described withrespect to FIG. 3 . The technique displays map 400 by optionallyoverlaying network components onto the geographical elements, therebyproviding the viewer with a more complete understanding of the networkand geography. For example, map 400 optionally includes visualrepresentations of one or more types of network elements: cable lines,taps, cable modems, amps, etc. For another example, map 400 optionallyincludes visual representations of one or more types of geographicalelements: roads, buildings, bodies of water, parks, etc. In the exampleof FIG. 4 , the technique displays visual indications 308, 312, and 318of modems on map 400. The technique differentiates between modemsexperiencing low downstream SNR and modems not experiencing lowdownstream SNR by using different shades or colors. In FIG. 4 , modems308 (indicated by lightning bolts) are displayed using one color (e.g.,a lighter color) because the technique has received responses from thosemodems indicating that the modems are not experiencing downstream SNRsbelow the threshold downstream SNR. In contrast, modems 312 and 318(indicated by lightning bolts) are displayed using another color (e.g.,a darker color) because the technique has received responses from thosemodems indicating that the modems are experiencing downstream SNRs belowthe threshold downstream SNR.

In FIG. 4 , modems 308 are grouped together as not experiencingdownstream SNRs below the threshold downstream SNR and modems 312 and318 are grouped together as experiencing downstream SNRs below thethreshold downstream SNR. As a result, the technique determines that thelocation of the source of the noise is at a location between tap 310 and306. The technique displays an indication 322 of the location of thesource of the noise on map 400, thereby indicating the area in the cablenetwork that should be investigated. In some examples, indication 322flashes to draw attention to the impairment. In some examples,indication 322 is a color different from the indications of the modems.

FIG. 5 illustrates an exemplary flow diagram 500, in accordance withsome embodiments. The technique for noise localization in a network isperformed at an electronic device (e.g., a computer) with a display,memory for storing computer instructions for the process, and one ormore processors for executing the computer instructions.

At block 502, the electronic device identifies one or more (e.g., aplurality of) channels (e.g., upstream channels) that are affected byupstream noise on the network. In some embodiments, the one or morechannels are identified as being affected by noise when the one or morechannels exhibit noise characteristics that exceed an upstream noisethreshold (e.g., lower SNR than a threshold SNR). For example, the CMTSdetermines that a modulation error rate is higher than a thresholdmodulation error rate. In some embodiments, the technique continuouslyor repeatedly monitors the network for upstream channels that areaffected by noise. In some embodiments, in response to detecting thatone or more channels are affected by upstream noise on the network, anotification is provided (e.g., displayed, transmitted) that identifiesthe one or more channels.

At block 504, the electronic device identifies (e.g., in response toidentifying the one or more channels that are affected by upstreamnoise) a plurality of devices (e.g., modems, cable modems, setup boxes)on the network that are attached to the one or more channels that areaffected by upstream noise (e.g., the one or more channels experiencingupstream noise that exceeds the upstream noise threshold).

At block 506, the electronic device displays a map (e.g., a geographicalmap that shows roads and structures, a network map that shows thelocation and connectivity of network elements (such as modems) withrespect to each other, combined geographic/network map that displaysgeographic features and the relative locations of network elements). Insome embodiments, the map includes information about the hierarchy ofthe network elements and, optionally, their relationship the modem macaddresses.

At block 508, subsequent to (e.g., in response to) identifying the oneor more channels that are affected by upstream noise (and, optionally,subsequent to (e.g., in response to) identifying the plurality ofdevices that are attached to the one or more channels that are affectedby upstream noise), the electronic device determines, for at least twodevices of (e.g., for each device of) the plurality of devices that areattached to the one or more channels, respective noise scores (e.g.,based on various combinations downstream noise scores and/or upstreamnoise scores, without determining downstream/upstream noise scores fordevices on the network that are not attached to the one or more channelsthat are affected by the upstream noise).

At block 510, subsequent to determining the respective noise score, theelectronic device displays, on the map, visual indications of the atleast two devices (e.g., all devices) of the plurality of devices thatare attached to the one or more channels (e.g., without displayingvisual indications for devices not connected to the one or more channelsthat are affected by upstream noise). In accordance with adetermination, at block 512, that the determined noise score (e.g.,upstream SNR, downstream SNR) of a respective device is within a firstnoise score range (e.g., more than a threshold amount of noise), thevisual indication of the respective device is displayed, at block 514,with a first characteristic (e.g., a first appearance, a first color, afirst size, a first brightness) without having a second characteristic(e.g., thereby allowing for visual differentiation between devices withdetermined noise scores within the first noise score range and those notwithin the first noise score; without having a second appearance, asecond color, a second size, a second brightness). In some embodiments,the visual indication of the respective device with the firstcharacteristic also does not have a third characteristic. In accordancewith a determination, at block 512, that the determined noise score ofthe respective device is within a second noise score range (e.g., notmore than the threshold amount of noise), the visual indication of therespective device is displayed, at block 516, with the secondcharacteristic and without having the first characteristic (e.g., asecond color different from the first color, a second size differentfrom the first size, a second brightness different from the firstbrightness). In some embodiments, the visual indication of therespective device with the second characteristic also does not have thethird characteristic. In some embodiments, the ranges of the first noisescore range, the second noise score range, and the third noise scorerange do not overlap. Importantly, the visual indications do not reflectwhether a device on the network is noisy or not (e.g., do not reflectwhether the device is generating the noise).

In some embodiments, determining respective noise scores includesdetermining respective downstream noise scores (e.g., downstream SNR,downstream modulation error rate) for the respective devices.

In some embodiments, determining respective noise scores includesdetermining respective upstream noise scores (e.g., upstream SNR,upstream modulation error rate) for the respective devices. In someembodiments, the technique determines both upstream and downstream noisescores for the plurality of devices and groups the devices using bothmetrics.

In some embodiments, determining respective noise scores includesaccessing (e.g., polling for) SNR values (e.g., modulation error rates)for the respective devices. In some embodiments, the downstream noisescore is the downstream SNR for the respective device.

In some embodiments, determining respective noise scores includes using(e.g., calculating) a Noise Spectral Density (e.g., for downstreamfrequencies, for upstream frequencies) for the respective devices. Insome embodiments, the downstream noise score is the (downstream) NoiseSpectral Density for the respective device.

In some embodiments, determining respective noise scores includes using(e.g., determining) a full band spectrum (e.g., in downstreamfrequencies, in upstream frequencies) for the respective devices. Insome embodiments, the downstream noise score is the downstream full bandspectrum for the respective device.

In some embodiments, the electronic device identifies a first area(e.g., a geographical area, a portion of the network, a network segment,a network element) as including a first noise source. In someembodiments, the first area that is identified as including the firstnoise source is identified based on being an area between a first deviceof the plurality of devices that has a determined noise score that iswithin the first noise score range and a second device of the pluralityof devices that has a determined noise score that is within the secondnoise score range. In some embodiments, the area that is identified asincluding the first noise source is identified based on identifying aportion of the network that resides between a first group of devices ofthe plurality of devices that have determined noise scores that arewithin the first noise score range and a second group of device of theplurality of devices that have determined noise score that are withinthe second noise score range. In some embodiments, the techniquedisplays a visual indication (highlight the area with a flashing orbright color) of the first area identified as including the noise firstsource.

In some embodiments, the at least two respective devices are at leastthree respective devices of the plurality of devices that are attachedto the one or more channels, and displaying, on the map, visualindications of the at least two respective devices (e.g., all devices)of the plurality of devices that are attached to the one or morechannels (e.g., without displaying visual indications for devices notconnected to the one or more channels that are affected by upstreamnoise) includes: in accordance with a determination, at block 512, thatthe determined noise score of the respective device is within a thirdnoise score range, the visual indication of the respective device isdisplayed, at block 518, with a third characteristic (e.g., a thirdappearance, a third color, a third size, a third brightness) withouthaving the first characteristics and without having the secondcharacteristic (e.g., thereby allowing for visual differentiation amongdevices with determined noise scores within the first noise score range,within the second noise score range, and within the third noise scorerange).

In some embodiments, the electronic device identifies a second area(e.g., a geographical area, a portion of the network, a network segment,a network element) as including a second noise source. In someembodiments, the second area that is identified as including the secondnoise source is identified based on being an area between the seconddevice of the plurality of devices that has a determined noise scorethat is within the second noise score range and a third device of theplurality of devices that has a determined noise score that is withinthe third noise score range. In some embodiments, the area that isidentified as including the noise source is identified based onidentifying a portion of the network that resides between the secondgroup of devices of the plurality of devices that have determined noisescores that are within the second noise score range and a third group ofdevices of the plurality of devices that have determined noise scorethat are within the third noise score range. In some embodiments, thetechnique displays a visual indication of the second area identified asincluding the second noise source.

In some embodiments, the visual indications of the respective devices ofthe plurality of devices that are attached to the one or more channelsare based on values according to the proximity of the respective devicesto a source noise (e.g., the first noise source and/or the second noisesource). In some embodiments, each device (e.g., modem) is assigned avalue according to its proximity to the noise source, with each valuecorresponding to a characteristic (e.g., color gradient: ranging fromblue to green to yellow to orange to red with red identifying modemsthat are closer to the noise source and blue indicating modems that arefurther from the noise source, color intensity gradient: more intensecolors identifying modems that are closer to the noise source andlighter colors identifying modems that are farther from the noisesource). Thus, the characteristic of the visual indication of respectivedevices on the map show the proximity of the respective modems to thenoise source. In some embodiments, the proximity of the respectivedevices to the noise source is determined based on analysis of themodem's noise score.

In some embodiments, the electronic device determines, for at least thetwo devices of (e.g., for each device of) the plurality of devices thatare attached to the one or more channels, respective noise scores (e.g.,upstream noise scores and/or downstream noise scores) for a plurality oftimes (e.g., periodically, once a minute, once a day, once an hour for aweek). The electronic device stores the respective noise scores for theplurality of times (e.g., in a local or remote database).

In some embodiments, the technique also stores, for each of theplurality of times, additional metrics (e.g., which devices are active,what the weather is at the device locations, whether a network parameteris enabled/disabled, the value of various network parameters) as part ofa noise history.

In some situations, the noise in the network has an intermittent nature,appearing for a duration of time and disappearing for a duration oftime. In some embodiments, the technique stores information forlocalizing noise coming from more than one source and location. Thisnoise history can be used to detect the noise signature and to determinehow the noise varies over times (e.g., when the noise exists, when it isreduced, when it does not exist). In some embodiments, the map isconcurrently displayed with a control element (e.g., a slider). Thepositions on the control element correspond to various times (e.g.,times at which noise information was stored for the plurality ofdevices). Input is received at the control element (e.g., mouse input tomove a selected along the slider). As the input is received at thecontrol element, the device updates display of the characteristics ofthe respective devices to correspond to various downstream noise scorescorresponding to various times (of the historical data). Thus, the usercan see how the noise impacts the devices on the network over time.

In some embodiments, the technique automatically removes orde-emphasizes representations of devices from the map when the noisescores for those devices vary less than a threshold variance amount.Thus, the map provides a clearer representation of the devices that havenoise scores that vary over the plurality of times.

FIGS. 6A-6D illustrate exemplary user interfaces for analyzing anetwork, in accordance with some embodiments. The techniques describedwith respect to these figures are performed by an electronic device(e.g., a computer) with a display. The electronic device includes one ormore processors and memory. The memory includes one or more programsconfigured to be executed by the one or more processors. The one orprograms include instructions for performing the techniques, as outlinedbelow.

At FIG. 6A, the computer concurrently displays network graph 600,extended network graph 640, and map 650. Network graph 600 includesplotted lines 602 and 604. In some examples, lines 602 and 604correspond to SNR of a channel in dB (Y-axis) as plotted over time(X-axis). In some examples, lines 602 and 604 correspond to CodewordError Rate (CER) of a channel in quantity (or percentage) (Y-axis) asplotted over time (X-axis). In some examples, line 602 corresponds toSNR of a channel in dB (Y-axis) as plotted over time (X-axis) and line604 corresponds to CER of the channel in quantity (or percentage)(Y-axis) as plotted over time (X-axis). For example, the channel is anupstream channel. In some examples, network graph 600 concurrentlyincludes a plurality of plotted lines representing: (1) SNR of a firstupstream channel, (2) CER of the first upstream channel, (3) SNR of asecond upstream channel, and (4) CER of the second upstream channel.

The computer has received user input identifying time 8:40 pm on networkgraph 600, such as through activation of a computer mouse while themouse pointer is located at the 8:40 pm location on the network graph600. As a result, the computer has displayed selection line 606 and timeindicator 608, both of which correspond to (and indicate) the selectionof time 8:40 in network graph 600.

Extended network graph 640 provides a view of network information 644that extends beyond the view provided by network graph 600. Selectionwindow 642 indications which portion (e.g., corresponding to a certainduration) of extended network 640 is currently being displayed innetwork graph 600. For example, graphed network information 644 inselection 642 may be based on a combination (e.g., sum, avg) of the datarepresented by lines 602 and 604. User input to move selection window642 causes a corresponding update of the graphs in network graph 600.User input that increases or decreases the size (width) of selectionwindow 642 causes a corresponding display in network graph 600. As aresult, a user can provide input (e.g., by dragging the sides ofselection window 642) to increase or decrease the duration of timerepresented in network graph 600.

Map 650 provides a visual display of a geographical area, such as aportion of a city. The area actively displayed can be translated, zoomedin, and zoomed out based on user input. Map 650 includes representationsof roads 652 a-652 b and buildings 654 a-654 c, situated in accordancewith the represented geographical location (e.g., city). Map 650 alsoincludes visual representations of cable modems 660-664 deployed withinthe geographical area. In this example, three buildings 654 a-654 c aredisplayed along with the corresponding cable modems 660-664 deployed atthose buildings.

Based on the user selection of time 8:40 pm in network graph 600, thecomputer has displayed cable modems 660-664 with particular visualcharacteristics-some devices are displayed in one color while otherdevices are displayed in a different color. For example, the computeraccesses historical network information for modem 662 c for 8:40 pm onthe relevant (e.g., selected) day. The computer calculates a noise scorefor modem 662 c based on multiple parameters of modem 662 c at 8:40 pm.In some examples, the noise score is calculated using a plurality ofparameters particular to modem 662 c for 8:40 pm, the parametersselected from among the above-described categories: (1) parametersobtained directly from the modems and/or CMTSes, (2) parameterscalculated using category 1 parameter values (e.g., parameters obtaineddirectly from modems/CMTSes), (3) parameters obtained by analyzingvariations of the category 1 and 2 parameters over time (e.g., over asingle channel), (4) parameters obtained by analyzing variations ofparameters of category 1 and 2 over multiple channel frequencies (e.g.,at a single point in time), (5) parameters obtained from combining theparameters in categories 1, 2, 3 and 4, and (6) calculated parametersthat show dependencies and/or correlation between the parameters in anytwo or more of categories 1, 2, 3, 4 and 5. For example, the computercalculates a noise score for each of the modems 660-664.

The computer displays cable modems 660-664 with visual characteristicsbased on their respective calculated noise scores. Devices that have anoise score that exceeds a threshold value are displayed in dark colors,such as devices 660 a-660 d, 662 a, 662 c-662 d. Devices that have anoise score that does not exceed the threshold value are displayed inlight colors, such as devices 662 b and 664 a-664 d. This provides theuser with a visual indication of the noise score of the devices at theselected time (8:40 pm). Accordingly, the user can better understandwhere noise may be entering the network and negatively affecting theperformance (and thus noise score and health) of the modems on thenetwork.

At FIG. 6B, the computer receives user input identifying time 11:10 amon network graph 600, such as through activation of a computer mousewhile the mouse pointer is located at the 11:10 am location on thenetwork graph 600. As a result, the computer displays selection line 610and time indicator 612, both of which correspond to (and indicate) theselection of time 11:10 am in network graph 600. In addition, map 650 isupdated to reflect the noise scores of modems 660-664 for time 11:10 amof the relevant (e.g., selected) day. The computer updates the colors ofmodems 660-664 based on calculated respective noise scores for themodems for 11:10 am. In this example, modems 660 continue to exceed thethreshold value and continue to be displayed in the dark colors andmodems 664 continue to not exceed the threshold value and continue to bedisplayed in the light colors. Importantly, the colors of some of thegroup of modems in building 654 b has changed based on their noise scoreexceeding or not exceeding the threshold value. For example, the noisescores for modems 662 c and 662 d do not exceed the threshold value and,as a result, modems 662 c and 662 d are now displayed in a light color,as compared to previously being displayed in a dark color. For anotherexample, the noise scores for modem 662 b does exceed the thresholdvalue and, as a result, modem 662 b is now displayed in a dark color, ascompared to previously being displayed in a light color. These changesin the noise score as different historical times are selected foranalysis of network parameters indicate that a source of networkimpairment exists at or near those modems (modems 662 a, 662 c, 662 d).

At FIG. 6C, the computer receives user input selected modem 662 c and,in response, displays a pop-over details window 670 that includesdetails about modem 662 c, including a MAC address of the modem, astreet address at which the modem is deployed, an account numbercorresponding to the modem, a name of a customer corresponding to theaccount/modem, and an IP address corresponding to the modem.Accordingly, once the user identifies a potentially problematic modem,the user can quickly and efficiently access details about thepotentially problematic modem so that any network impairments can beaddressed quickly.

FIG. 6D is an alternative to FIG. 6B. In contrast to FIG. 6B, displaysof map 500, selection line 606, and/or time indicator 608 are maintainedwhen the computer receives the user input identifying time 11:10 am onnetwork graph 600. Further, the computer displays, in response toreceiving the user input identifying time 11:10 am, an additionalselection line 610 and/or time indicator 610. Map 650 continues toreflect the noise scores of modems 660-664 for time 8:40 pm of therelevant (e.g., selected) day. In response to receiving the user inputidentifying time 11:10 am, second map 680 is updated (or newlydisplayed) and reflects the noise scores of modems 660-664 for time11:10 am of the relevant (e.g., selected) day. Thus, the user cancompare the status of the modems between the two selected times usingthe concurrently displayed maps, which correspond to the same map area.

FIG. 7 illustrates an exemplary flow diagram 700, in accordance withsome embodiments. The technique for analyzing a network is performed atan electronic device (e.g., a computer) with an optional display, memoryfor storing computer instructions for the process, and one or moreprocessors for executing the computer instructions.

At block 702, the technique concurrently displays: (a) a graphicalrepresentation (e.g., 602, 604) (or graphical representations) of a (ora plurality of) network quality metric (e.g., signal-to-noise ratio(SNR) based on signals received from multiple network devices (such asmultiple (or all) network devices of the plurality of network devices);Codeword Error Rate (CER) (corrected and/or uncorrectables) based onsignals received from multiple network devices (such as multiple (orall) network devices of the plurality of network devices)) graphedagainst a first duration of time (e.g., for one hour, from 9 am to 10am) for a signal (or a plurality of signals) (e.g., a first signalcorresponding to a first upstream channel, a second signal correspondingto a second upstream channel different from the first upstream channel,on which the plurality of network devices are deployed) and (b) a map(e.g., 650) of an area. The map (e.g., 650) includes concurrent displayof: one or more geographical elements (e.g., roads, structures 654) ofthe area that are not network devices, and a plurality of networkdevices (e.g., 660, 662, 664). For example, the graphical representation(e.g., 602, 604) (or graphical representations) of a (or a plurality of)network quality metric is an SNR vs. time graph is displayed thatincludes (1) an average SNR for network devices on the first upstreamchannel and/or (2) an average SNR for network devices on the secondupstream channel, both over the same time duration (e.g., over an hour).

At block 706, while displaying the graphical representation (orgraphical representations) of the network quality metric for the signal(or a plurality of signals), the technique receives input selecting afirst time (e.g., 608, 612) that is within the first duration of time.In some examples, an indication, such as a vertical line, is shown inresponse to receiving the input selecting the first time to indicate tothe user the selected time. The vertical line optionally crosses thegraphical representations of the plurality of signals at a locationcorresponding to the selected time.

At block 708, in response to receiving the input selecting the firsttime, the technique updates the map of the area to change a visualcharacteristic (e.g., a color, a size, a shape) of at least some of thedisplayed plurality of network devices (e.g., 660, 662, 664) based on arespective noise score for the corresponding network devices at theselected first time (e.g., and not based on a noise score for any othernetwork device).

In accordance with some embodiments, the respective noise score for anetwork device is determined based on a plurality of network parameters,the plurality of network parameters including a first network parameterobtained directly from the network device and a second network parameterobtained by analyzing variations in a network parameter of the networkdevice over time.

In accordance with some embodiments, the respective noise score for anetwork device is determined based on a plurality of network parameters,the plurality of network parameters including a first network parameterobtained directly from the network device and a third network parameterobtained by analyzing variations in a network parameter of the networkdevice over multiple channel frequencies.

In accordance with some embodiments, the respective noise score for anetwork device is determined based on a plurality of network parameters,the plurality of network parameters including a first network parameterobtained directly from the network device and a fourth network parameterobtained by analyzing a dependency or correlation between at least twonetwork parameters of the network device (e.g., at a point in time, fora single channel, over time, over multiple channels).

In accordance with some embodiments, updating the map of the area tochange a visual characteristic (e.g., a color, a size, a shape) of atleast some of the displayed plurality of network devices based on thenoise score for the corresponding network devices at the selected firsttime comprises: determining a respective noise score for each of theplurality of network devices for the selected first time; determiningwhether the respective noise score for each respective network devicesof the plurality of network devices meets a noise score criteria (e.g.,noise score exceeds a threshold noise value, SNR is more than athreshold SNR value), updating the map of the area such that: respectivenetwork devices of the plurality of network devices that meet the devicenoise score criteria are displayed using a first visual appearance(e.g., a first color, a first size, a first shape) (without displayingthose network devices with a second visual appearance), and respectivenetwork devices of the plurality of network devices that do not meet thenoise score criteria are displayed using a second visual appearancedifferent from the first visual appearance (e.g., a second color, asecond size, a second shape) (without displaying those network deviceswith the first visual appearance).

In accordance with some embodiments, updating the map of the area tochange a visual characteristic (e.g., a color, a size, a shape) of atleast some of the displayed plurality of network devices includeschanging the visual characteristic of at least some network devices andmaintaining the visual characteristic of at least some network devices.

In accordance with some embodiments, the technique receives selection ofa network device of the plurality of network devices. In response toreceiving selection of the network device, concurrently displaying twoor more (or all) of (e.g., 670): a MAC address of the network device, astreet address of the network device (e.g., the physical address atwhich the device is located, such as the address reflected in the map),and an account number of the network device (e.g., the account numbercorresponding to a network account for which a subscriber isresponsible).

In accordance with some embodiments, the technique displays,concurrently with the graphical representation of a network qualitymetric graphed against time for a signals, second graphicalrepresentation (e.g., 640) of the network quality metric graphed(independent of the first graphical representation of the networkquality metric) against a second duration of time (e.g., 8 am to 11 am),wherein the second duration of time includes the first duration of time,and wherein the second duration of time is longer than the firstduration of time.

In accordance with some embodiments, the technique displays,concurrently with the graphical representation of the network qualitymetric for the signal, a graphical representation of the network qualitymetric (e.g., signal-to-noise ratio (SNR) based on signals received frommultiple network devices (such as multiple (or all) network devices ofthe plurality of network devices); Codeword Error Rate (CER) (correctedand/or uncorrectables) based on signals received from multiple networkdevices (such as multiple (or all) network devices of the plurality ofnetwork devices)) graphed against the first duration of time (e.g., forone hour, from 9 am to 10 am) for a second signal (e.g., a second signalcorresponding to a second upstream channel different from the firstupstream channel; on which the plurality of network devices aredeployed). For example, an SNR vs. time graph is displayed that includesan average SNR for network devices on the second upstream channel overthe same time duration as the first signal (e.g., over an hour).

In accordance with some embodiments, the graphical representation of thenetwork quality metric for the signal is in a first color (e.g., a firstupstream channel). The graphical representation of the network qualitymetric for the second signal is in a second color (e.g., a secondupstream channel different from the first upstream channel), the secondcolor being different from the first color.

In accordance with some embodiments, while displaying the graphicalrepresentation (or graphical representations) of the network qualitymetric for the signal (or a plurality of signals) and the map of thearea, the technique receives input selecting a second time (e.g., 612)that is within the first duration of time. In some examples, anindication, such as a second vertical line (e.g., 610), is shown inresponse to receiving the input selecting the second time to indicate tothe user the selected time. The vertical line optionally crosses thegraphical representations of the plurality of signals at a locationcorresponding to the selected second time. In response to receiving theinput selecting the second time, the technique displays a second map(e.g., 680) of the area, concurrently with the first map of the area,that includes at least some of the displayed plurality of networkdevices with a visual characteristic (e.g., a color, a size, a shape)based on the respective noise score for the corresponding networkdevices at the selected second time (e.g., and not based on a noisescore for any other network device).

In accordance with some embodiments, a respective noise score for arespective network device is calculated based on a make (or model) ofthe network device. In some examples, a noise score for a first networkdevice is calculated differently from a noise score for a second networkdevice based on the two network devices having different makes (ormodels) (even for the same noise score type). Thus, the noise score forthe first network device is calculated with one set of weightings of thenetwork parameters and the noise score for the second network device iscalculated with a second set of weightings of the network parameters.

In accordance with some embodiments, determining a respective noisescore for a respective network device for a time includes: determining anoise score type that is currently selected; in accordance with adetermination that a first noise score type is currently selected:using, based on the first noise score type, a first set of networkparameters (e.g., including a first network parameter) for therespective network device for the time to calculate the respective noisescore (e.g., without using a second set of network parameters (e.g.,that includes a second network parameter)); in accordance with adetermination that a second noise score type is currently selected:using, based on the second noise score type, a second set of networkparameters (e.g., including a second network parameter, the second setbeing different from the first set of network parameters) for therespective network device for the time to calculate the respective noisescore (e.g., without using the first set of network parameters thatinclude the first parameter).

For example, the first noise score type may be a calculation of anamount of correlation between power level and NMTER of a respectivedevice for a time. For another example, the second noise score type maybe a calculation of power level divided by upstream SNR of therespective network device. Thus, different calculations using differentnetwork parameters are used to determine a noise score for a respectivedevice based on the selected noise score type.

In accordance with some embodiments, subsequent to determining therespective noise score types for the respective devices and subsequentto updating the map of the area to change the visual characteristic(e.g., a color, a size, a shape) of at least some of the displayedplurality of network devices based on the respective noise score for thecorresponding network devices at the selected first time (e.g., and notbased on a noise score for any other network device), the techniquereceives input to change the noise score type. In response to receivinginput to change the noise score type, the technique updates the map ofthe area to change the visual characteristic (e.g., a color, a size, ashape) of at least some of the displayed plurality of network devicesbased on the updated respective noise score for the correspondingnetwork devices at the selected time.

Thus, the user can provide input to select the noise score type therebychanging how the noise scores are calculated for the network devices.The change in the noise scores causes a corresponding change in how thenetwork devices are displayed on the map. In some embodiments, the usercan selected from among 5, 10, or more noise score types. By changingamong various noise score types, the user can determined which (orwhether any) of the noise score types produces results indicating thenetwork impairment.

FIGS. 8A-8D illustrate exemplary user interfaces for analyzing anetwork, in accordance with some embodiments. The techniques describedwith respect to these figures are performed by an electronic device(e.g., a computer) with a display. The electronic device includes one ormore processors and memory. The memory includes one or more programsconfigured to be executed by the one or more processors. The one orprograms include instructions for performing the techniques, as outlinedbelow.

At FIG. 8A, the computer concurrently displays network graph 600,extended network graph 640, and map 650. The computer has received inputactivating noise change option 800, which enables the user to moreeasily visual changes in the noise score of modems. Slider 802 indicatesthat the change threshold is set at 2%.

The computer then receives user input identifying time 11:10 am onnetwork graph 600, such as through activation of a computer mouse whilethe mouse pointer is located at the 11:10 am location on the networkgraph 600. As a result, the computer displays selection line 610 andtime indicator 612, both of which correspond to (and indicate) theselection of time 11:10 am in network graph 600. The computerdetermines, for each modem 660-664 a difference between the respectivemodem's noise score for time 8:40 pm and for time 11:10 am. The computerthen visually differentiates between modems that have a difference thatmeets the change threshold set using slider 802 as part of noise changeoption 800.

In some examples, as illustrated in FIG. 8B, the computer displaysmodems 662 c-662 d that meet the change threshold and ceases to displaymodems 660, 662 a-662 b, and 664 that do not meet the change threshold.This provides the user with a visual indication of devices that have hadlarge changes in their noise score between the two selected times.Accordingly, the user can better understand where noise may be enteringthe network and negatively affecting the performance (and thus noisescore and health) of the modems on the network.

In some examples, as illustrated in FIG. 8C, the computer displaysmodems 662 c-662 d that meet the change threshold using a darker colorand displays modems 660, 662 a-662 b, and 664 that do not meet thechange threshold using a lighter color. Thus, modems 662 c-662 d eachexperienced a change in their respective noise score that exceeded 2%between the two selected times. Modems 660, 662 a-662 b, and 664 eachexperienced a change in their respective noise score that did not exceed2% between the two selected times. This provides the user with a visualindication of devices have had large changes in their noise scorebetween the two selected times. Accordingly, the user can betterunderstand where noise may be entering the network and negativelyaffecting the performance (and thus noise score and health) of themodems on the network.

In some examples (e.g., as can be implemented in both FIGS. 8B and 8C),modems are also visually differentiated based on the degree to whichthere is a difference between the respective modem's noise score fortime 8:40 pm and for time 11:10 am. In FIG. 8C, for example, modem 662 ccan be a darker color than modem 662 d when the determined change formodem 662 c is more than the determined change for modem 662 d.Similarly, modems that do not meet the change threshold can also besimilarly visually differentiated based on the degree to which there isa difference between the respective modem's noise score for time 8:40 pmand for time 11:10 am.

While the computer is displaying the interface of FIG. 8C, the computerreceives user input that updates the change threshold to be set at 1%,as shown in FIG. 8D. For example, the computer receives mouse inputactivating slider 802 to indicate the change threshold is set at 1%. Inresponse, the computer updates map 650, as shown in FIG. 8D, to visuallydifferentiate between modems 662 that meet the updated 1% changethreshold (e.g., using a dark color) and modems 660 and 664 that do notmeet the updated 1% change threshold (e.g., using light colors or notdisplaying those modems). This provides the user with a visualindication of devices that had at least the threshold amount of changein their noise score between the two selected times. Accordingly, theuser can better understand where noise may be entering the network andnegatively affecting the performance (and thus noise score and health)of the modems on the network.

Once the user identifies a potentially problematic modem using thistechnique, the user can quickly and efficiently access details about thepotentially problematic modem so that any network impairments can beaddressed quickly.

FIGS. 9A-9B illustrates an exemplary flow diagram 900, in accordancewith some embodiments. The technique for analyzing a network isperformed at an electronic device (e.g., a computer) with an optionaldisplay, memory for storing computer instructions for the process, andone or more processors for executing the computer instructions.

At block 902, the technique concurrently displays: (a) a graphicalrepresentation (e.g., 602, 604) of a network quality metric (e.g.,signal-to-noise ratio (SNR) based on signals received from multiplenetwork devices (such as multiple (or all) network devices of theplurality of network devices): Codeword Error Rate (CER) (correctedand/or uncorrectables) based on signals received from multiple networkdevices (such as multiple (or all) network devices of the plurality ofnetwork devices)) graphed against a first duration of time (e.g., forone hour, from 9 am to 10 am) for a signal (e.g., a first signalcorresponding to a first upstream channel on which the plurality ofnetwork devices are deployed) and (b) a map (e.g., 650) of an area. Forexample, graphical representation (e.g., 602, 604) of a network qualitymetric is an average SNR for network devices on an upstream channel vs.time graph is displayed that includes SNR for the first upstream channelover the time duration (e.g., over an hour).

At block 904, the map includes concurrent display of, one or moregeographical elements (e.g., roads, structures 654) of the area that arenot network devices, and a plurality of network devices (e.g., 660, 662,664) (e.g., a cable modem, a cable modem termination system).

At block 906, w % bile displaying the graphical representation of thenetwork quality metric for the signals, the technique receives firstinput selecting a first time (e.g., 608) that is within the firstduration of time. In some examples, an indication, such as a verticalline, is shown in response to receiving the input selecting the firsttime to indicate to the user the selected time. The vertical lineoptionally crosses the graphical representation of the signal.

At block 908, in response to receiving the first input, the techniquedisplays a first visual indicator (e.g., 606) corresponding to the firsttime in the graphical representation of the network quality metric.

At block 910, while displaying the graphical representation of thenetwork quality metric for the signals, the technique receives secondinput selecting a second time (e.g., 612), different from the firsttime, that is within the first duration of time. In some examples, anindication, such as a vertical line, is shown in response to receivingthe input selecting the second time to indicate to the user the selectedtime. The vertical line optionally crosses the graphical representationof the signal.

At block 912, in response to receiving the second input, the techniquedisplays a second visual indicator (e.g., 610) corresponding to thesecond time in the graphical representation of the network qualitymetric.

At block 914, subsequent to (e.g., in response to) receiving the firstinput and the second input, the technique performs blocks 916-924.

At block 916, the technique determines a change in a noise score foreach of the plurality of network devices between the first time and thesecond time.

At block 918, the technique determines whether the respective change inthe noise score for each respective network device of the plurality ofnetwork devices meets a noise score change criteria (e.g., 802) (e.g.,change in noise score exceeds a threshold change in noise score, changein device SNR is more than a threshold SNR change value).

At block 920, the technique displays (e.g., updating), based on thedeterminations of whether respective changes in the noise scores meetthe noise score change criteria, the map (e.g., 650) of the area suchthat: (at block 922) respective network devices of the plurality ofnetwork devices that meet the noise score change criteria are displayedusing a first visual appearance (e.g., a first color, a first size, afirst shape) (without displaying those network devices with a secondvisual appearance) and, in accordance with some embodiments. (at block924) respective network devices of the plurality of network devices thatdo not meet the noise score change criteria are displayed using a secondvisual appearance different from the first visual appearance (e.g., asecond color, a second size, a second shape) (without displaying thosenetwork devices with the first visual appearance).

In accordance with some embodiments, subsequent to (e.g., in responseto) receiving the first input and the second input: the techniquedisplays (e.g., updating), based on the determinations of whetherrespective changes in the noise scores meet the noise score changecriteria, the map (e.g., 650) of the area such that: respective networkdevices of the plurality of network devices that do not meet the noisescore change criteria are not displayed (e.g., cease to be displayed).

In accordance with some embodiments, the noise score change criteria ismet for a respective network device when a change in the noise score ofthe network device between the first time and the second time exceeds athreshold change value (e.g., exceeds an amount of change as apercentage or in db). The threshold change value is provided by userinput (e.g., via 800, 802 the threshold change value is a thresholdchange that is user-programmable, such as via user input on a sliderelement).

In accordance with some embodiments, the noise score change criteria ismet for a respective network device when the respective network deviceis categorized has having an amount of change in the noise score of thenetwork device between the first time and the second time that fallswithin a top number of network devices (e.g., display/identify thenetwork devices with the largest changes in noise score, such as the top5 or top 10 devices with the most change). In accordance with someembodiments, the threshold change value is provided by user input (e.g.,the threshold change value is a threshold change that isuser-programmable, such as via user input on a slider element).

In accordance with some embodiments, the visual appearance of respectivenetwork devices of the plurality of network devices (e.g., that meet thenoise score change criteria, that do not meet the noise score changecriteria) are displayed using a variable value (e.g., varying intensity,varying brightness, varying size). The variable value is based on amagnitude of the change in the noise score of the network device betweenthe first time and the second time. In some examples, a network devicethat has a larger change in SNR between the first time and the secondtime is displayed in a darker color (e.g., dark blue) than a networkdevice that has a smaller change in SNR between the first time and thesecond time (e.g., displayed in light blue), although both networkdevices exceed the threshold value.

In accordance with some embodiments, the technique receives selection ofa network device of the plurality of network devices. Tin response toreceiving selection of the network devices, concurrently displaying twoor more (or all) of (e.g., 670): a MAC address of the network device, astreet address of the network device (e.g., the physical address atwhich the device is located), and an account number of the networkdevice (e.g., the account number corresponding to a network account forwhich a subscriber is responsible).

In accordance with some embodiments, the technique displays,concurrently with the graphical representation of the network qualitymetric graphed against time for the signal, second graphicalrepresentation (e.g., 640) of the network quality metric graphed againsta second duration of time (e.g., 8 am to 11 am), wherein the secondduration of time includes the first duration of time, and wherein thesecond duration of time is longer than the first duration of time.

In accordance with some embodiments, the technique displays,concurrently with the graphical representation of the network qualitymetric for the signal, a graphical representation of the network qualitymetric (e.g., signal-to-noise ratio (SNR) based on signals received frommultiple network devices (such as multiple (or all) network devices ofthe plurality of network devices); Codeword Error Rate (CER) (correctedand/or uncorrectables) based on signals received from multiple networkdevices (such as multiple (or all) network devices of the plurality ofnetwork devices)) graphed against the first duration of time (e.g., forone hour, from 9 am to 10 am) for a second signal (e.g., a second signalcorresponding to a second upstream channel different from the firstupstream channel; on which the plurality of network devices aredeployed). For example, an SNR vs. time graph is displayed that includesan average SNR for network devices on the second upstream channel overthe same time duration as the first signal (e.g., over an hour).

In accordance with some embodiments, the graphical representation of thenetwork quality metric for the signal is in a first color (e.g., a firstupstream channel). The graphical representation of the network qualitymetric for the second signal is in a second color (e.g., a secondupstream channel different from the first upstream channel), the secondcolor being different from the first color.

In accordance with some embodiments, a respective noise score for arespective network device is calculated based on a make (or model) ofthe network device. In some examples, a noise score for a first networkdevice is calculated differently from a noise score for a second networkdevice based on the two network devices having different makes (ormodels) (even for the same noise score type). Thus, the noise score forthe first network device is calculated with one set of weightings of thenetwork parameters and the noise score for the second network device iscalculated with a second set of weightings of the network parameters.

In accordance with some embodiments, determining a respective noisescore for a respective network device for a time includes: determining anoise score type that is currently selected; in accordance with adetermination that a first noise score type is currently selected:using, based on the first noise score type, a first set of networkparameters (e.g., including a first network parameter or including acategory 1 parameter, as described above) for the respective networkdevice for the time to calculate the respective noise score (e.g.,without using a second set of network parameters (e.g., that includes asecond network parameter) or without including a category 3 parameter,as described above); in accordance with a determination that a secondnoise score type is currently selected: using, based on the second noisescore type, a second set of network parameters (e.g., including a secondnetwork parameter, the second set being different from the first set ofnetwork parameters, or including a category 3 parameter, as describedabove) for the respective network device for the time to calculate therespective noise score (e.g., without using the first set of networkparameters that include the first parameter or without including anycategory 1 parameter, as described above).

For example, the first noise score type may be a calculation of anamount of correlation between power level and NMTER of a respectivedevice for a time. For another example, the second noise score type maybe a calculation of power level divided by upstream SNR of therespective network device. Thus, different calculations using differentnetwork parameters are used to determine a noise score for a respectivedevice based on the selected noise score type.

In accordance with some embodiments, subsequent to displaying (e.g.,updating), based on the determinations of whether respective changes inthe noise scores meet the noise score change criteria, the map of thearea, the technique receives input to change the noise score type. Inresponse to receiving input to change the noise score type: thetechnique determines updated respective noise scores for respectivenetwork devices for the first time and the second time; and thetechnique determines whether a respective change in the noise score foreach respective network device of the plurality of network devices meetsthe noise score change criteria (e.g., change in noise score exceeds athreshold change in noise score, change in device SNR is more than athreshold SNR change value). The technique updates (e.g., updating),based on the determinations of whether respective changes in the noisescores meet the noise score change criteria, the map of the area tochange the visual characteristic (e.g., a color, a size, a shape) of atleast some of the displayed plurality of network devices.

Thus, the user can provide input to select the noise score type therebychanging how the noise scores are calculated for the network devices.The change in the noise scores causes a corresponding change in how thenetwork devices are displayed on the map. In some embodiments, the usercan selected from among 5, 10, or more noise score types. By changingamong various noise score types, the user can determined which (orwhether any) of the noise score types produces results indicating thenetwork impairment.

In accordance with some embodiments, a technique for analyzing a networkcomprises: determining a first time at which a first type of networkimpairment is negatively affecting the network; determining a secondtime at which the first type of network impairment is not negativelyaffecting the network or is negatively affecting the network less thanat the first time; calculating, for each of a plurality of networkdevices of the network: a first noise score for the first time using afirst calculation; a second noise score for the first time using asecond calculation different from the first calculation; a third noisescore for the second time using the first calculation; and a fourthnoise score for the second time using the second calculation;determining, for each of the plurality of network devices: a firstdifference score by calculating a difference between the first noisescore and the third noise score for the respective network device; and asecond difference score by calculating a difference between the secondnoise score and the fourth noise score for the respective networkdevice; identifying the first calculation as an indicator of the firsttype of network impairment when a subset of the plurality of networkdevices have first difference scores that exceed a threshold difference;and identifying the second calculation as an indicator of the first typeof network impairment when a subset of the plurality of network deviceshave second difference scores that exceed the threshold difference.

The foregoing description has been described with reference to specificembodiments. However, the illustrative discussions above are notintended to be exhaustive or to limit the invention to the precise formsdescribed. Many modifications and variations are possible in view of theabove teachings. Others skilled in the art are thereby enabled to bestutilize the techniques and various embodiments with variousmodifications as suited to various uses.

Although the disclosure and examples have been described with referenceto the accompanying drawings, it is to be noted that various changes andmodifications will become apparent to those skilled in the art. Suchchanges and modifications are to be understood as being included withinthe scope of the disclosure.

What is claimed is:
 1. A method for noise analysis in a network,comprising: determining, an amount of upstream noise on each of aplurality of upstream channels; identifying a noisy upstream channelbased on whether the determined amount of upstream noise on a respectivechannel of the plurality of upstream channels meets a noisy channelcriteria; and identifying a plurality of suspect devices associated withthe noisy upstream channel based on respective devices of the pluralityof suspect devices meeting a set of one or more suspect criteria,wherein the set of one or more suspect criteria includes a channelcriterion that is met for a respective device when the respective devicehas communicated on the noisy upstream channel.
 2. The method of claim1, wherein the amount of upstream noise is determined based at least onsignal to noise ratio (SNR) values and/or codeword error rates (CER). 3.The method of claim 1, wherein the set of one or more suspect criteriaincludes an upstream transmit power level criterion that is met for therespective device when a determined upstream transmit power level valuefor the respective device exceeds an upstream transmit power levelthreshold.
 4. The method of claim 1, wherein the set of one or moresuspect criteria includes an upstream transmit power level standarddeviation criterion that is met for the respective device when adetermined upstream transmit power level standard deviation value forthe respective device exceeds an upstream transmit power level standarddeviation threshold.
 5. The method of claim 1, wherein the set of one ormore suspect criteria includes an NMTER (non-main tap energy to totaltap energy ratio) criterion that is met for the respective device when adetermined NMTER value for the respective device exceeds an NMTERthreshold.
 6. The method of claim 1, wherein the set of one or moresuspect criteria includes an NMTER (non-main tap energy to total tapenergy ratio) standard deviation criterion that is met for therespective device when a determined NMTER standard deviation value forthe respective device exceeds an NMTER standard deviation threshold. 7.The method of claim 1, wherein the set of one or more suspect criteriaincludes a power level-to-NMTER (non-main tap energy to total tap energyratio) correlation criterion that is met for the respective device whena determined power level-to-NMTER correlation value for the respectivedevice exceeds a power level-to-NMTER correlation threshold.
 8. Themethod of claim 1, wherein the set of one or more suspect criteriaincludes a pre-equalizer criterion that is met for the respective devicebased on a pre-equalizer coefficient of the respective device.
 9. Themethod of claim 1, wherein the set of one or more suspect criteriaincludes a criterion that is met for the respective device when adetermined noise score for the respective device does not exceed athreshold.
 10. The method of claim 1, wherein the set of one or moresuspect criteria includes an SNR (signal-to-noise ratio) standarddeviation criterion that is met for the respective device when adetermined SNR standard deviation value for the respective deviceexceeds an SNR standard deviation threshold.
 11. The method of claim 1,wherein the set of one or more suspect criteria includes a CER (codeworderror rate) criterion that is met for the respective device when adetermined CER value for the respective device exceeds a CER threshold.12. The method of claim 1, further comprising: determining whether aquantity of devices of the plurality of suspect devices meets a quantitymetric; in accordance with a determination that the quantity of devicesof the plurality of suspect devices does not meet the quantity metric:adjusting one or more of: an upstream transmit power level threshold, anupstream transmit power level standard deviation threshold, an NMTER(non-main tap energy to total tap energy ratio) threshold, an NMTERstandard deviation threshold, an NMITE (non-main tap individual tapenergy) threshold, an SNR threshold, an SNR standard deviationthreshold, and a CER threshold; and subsequent to the adjusting,updating the identified plurality of suspect devices based on respectivedevices of the plurality of suspect devices meeting the set of one ormore suspect criteria with at least one adjusted threshold value. 13.The method of claim 1, wherein the noise scores indicate the likelihoodof the plurality of suspect devices causing noise above a noisethreshold and/or the likelihood of the plurality of suspect devicesbeing in proximity of a point of entry of noise into the network. 14.The method of claim 13, wherein determining noise scores for theplurality of suspect devices includes: assigning weights to a pluralityof network parameters for the plurality of suspect devices; andcalculating weight-adjusted noise scores for the plurality of suspectdevices, the weight-adjusted noise scores indicating the likelihood ofthe plurality of suspect devices causing noise above a noise thresholdand/or the likelihood of the plurality of suspect devices being inproximity of a point of entry of noise into the network.
 15. The methodof claim 14, wherein the plurality of network parameters are selectedfrom among: (a) Codeword Error Rate, (b) Micro Reflection Level, (c) CMPre-Equalized Frequency Response, (d) CMTS CM Equalized FrequencyResponse, (e) Main Tap Ratio, (f) Non Main Tap Energy to Total TapEnergy Ratio, (g) Power Level, (h) Power Level to MTR Ratio, (i) PowerLevel to NMTER Ratio, (j) Power Level to TTE Total Tap Energy Ratio, (k)TTE Total Tap Energy, (l) Non Main Individual Tap Energy, (m) Signal toNoise Ratio, (n) SNR spike level above certain threshold over time, and(o) SNR spike count above certain threshold over time for a period oftime.
 16. The method of claim 13, wherein determining noise scores forthe plurality of suspect devices includes: providing a plurality ofnetwork parameters for the plurality of suspect devices to a logisticregression model to calculate the noise scores for the plurality ofsuspect devices.
 17. The method of claim 13, wherein determining noisescores for the plurality of suspect devices includes: providing aplurality of network parameters for the plurality of suspect devices toa neural network machine learning model to calculate the noise scoresfor the plurality of suspect devices.
 18. A non-transitorycomputer-readable storage medium storing one or more programs for noiseanalysis in a network, the one or more programs configured to beexecuted by one or more processors of an electronic device, and the oneor more programs including instructions for: determining an amount ofupstream noise on each channel of a plurality of upstream channels;identifying a noisy upstream channel based on whether the determinedamount of upstream noise on a respective channel of the plurality ofupstream channels meet a noisy channel criteria; and identifying aplurality of suspect devices associated with the noisy upstream channelbased on respective devices of the plurality of suspect devices meetinga set of one or more suspect criteria, wherein the set of one or moresuspect criteria includes a channel criterion that is met for arespective device when the respective device has communicated on thenoisy upstream channel.
 19. The non-transitory computer-readable storagemedium of claim 18, wherein the set of one or more suspect criteriaincludes an SNR (signal-to-noise ratio) standard deviation criterionthat is met for the respective device when a determined SNR standarddeviation value for the respective device exceeds an SNR standarddeviation threshold.
 20. The non-transitory computer-readable storagemedium of claim 18, further comprising: determining whether a quantityof devices of the plurality of suspect devices meets a quantity metric;in accordance with a determination that the quantity of devices of theplurality of suspect devices does not meet the quantity metric:adjusting one or more of: an upstream transmit power level threshold, anupstream transmit power level standard deviation threshold, an NMTER(non-main tap energy to total tap energy ratio) threshold, an NMTERstandard deviation threshold, an NMITE (non-main tap individual tapenergy) threshold, an SNR threshold, an SNR standard deviationthreshold, and a CER threshold; and subsequent to the adjusting,updating the identified plurality of suspect devices based on respectivedevices of the plurality of suspect devices meeting the set of one ormore suspect criteria with at least one adjusted threshold value. 21.The non-transitory computer-readable storage medium of claim 18, whereinthe amount of upstream noise is determined based at least on signal tonoise ratio (SNR) values and/or codeword error rates (CER).
 22. Thenon-transitory computer-readable storage medium of claim 18, whereindetermining noise scores for the plurality of suspect devices includes:assigning weights to a plurality of network parameters for the pluralityof suspect devices; and calculating weight-adjusted noise scores for theplurality of suspect devices, the weight-adjusted noise scoresindicating the likelihood of the plurality of suspect devices causingnoise above a noise threshold and/or the likelihood of the plurality ofsuspect devices being in proximity of a point of entry of noise into thenetwork.
 23. The non-transitory computer-readable storage medium ofclaim 18, wherein determining noise scores for the plurality of suspectdevices includes: providing a plurality of network parameters for theplurality of suspect devices to a logistic regression model to calculatethe noise scores for the plurality of suspect devices.
 24. Thenon-transitory computer-readable storage medium of claim 18, whereindetermining noise scores for the plurality of suspect devices includes:providing a plurality of network parameters for the plurality of suspectdevices to a neural network machine learning model to calculate thenoise scores for the plurality of suspect devices.
 25. An electronicdevice, comprising: one or more processors; and memory storing one ormore programs for noise analysis in a network, the one or more programsconfigured to be executed by the one or more processors, and the one ormore programs including instructions for: determining an amount ofupstream noise on each channel of a plurality of upstream channels;identifying a noisy upstream channel based on whether the determinedamount of upstream noise for a respective channel of the plurality ofupstream channels meets a noisy channel criteria; and identifying aplurality of suspect devices associated with the noisy upstream channelbased on respective devices of the plurality of suspect devices meetinga set of one or more suspect criteria, wherein the set of one or moresuspect criteria includes a channel criterion that is met for arespective device when the respective device has communicated on thenoisy upstream channel.
 26. The electronic device of claim 25, whereinthe determination of the amount of upstream noise is based on SNR(signal-to-noise ratio) values and/or codeword error rates (CER). 27.The electronic device of claim 25, further comprising: determiningwhether a quantity of devices of the plurality of suspect devices meetsa quantity metric; in accordance with a determination that the quantityof devices of the plurality of suspect devices does not meet thequantity metric: adjusting one or more of: an upstream transmit powerlevel threshold, an upstream transmit power level standard deviationthreshold, an NMTER (non-main tap energy to total tap energy ratio)threshold, an NMTER standard deviation threshold, an NMITE (non-main tapindividual tap energy) threshold, an SNR threshold, an SNR standarddeviation threshold, and a CER threshold; and subsequent to theadjusting, updating the identified plurality of suspect devices based onrespective devices of the plurality of suspect devices meeting the setof one or more suspect criteria with at least one adjusted thresholdvalue.
 28. The electronic device of claim 25, further comprising:determining noise scores for the plurality of suspect devices, the noisescores indicating the likelihood of the plurality of suspect devicescausing noise above a noise threshold and/or the likelihood of theplurality of suspect devices being in proximity of a point of entry ofnoise into the network; and providing the noise scores.
 29. Theelectronic device of claim 28, wherein determining noise scores for theplurality of suspect devices includes: assigning weights to a pluralityof network parameters for the plurality of suspect devices; andcalculating weight-adjusted noise scores for the plurality of suspectdevices, the weight-adjusted noise scores indicating the likelihood ofthe plurality of suspect devices causing noise above a noise thresholdand/or the likelihood of the plurality of suspect devices being inproximity of a point of entry of noise into the network.
 30. Theelectronic device of claim 28, wherein determining noise scores for theplurality of suspect devices includes: providing a plurality of networkparameters for the plurality of suspect devices to a logistic regressionmodel to calculate the noise scores for the plurality of suspectdevices.
 31. The electronic device of claim 28, wherein determiningnoise scores for the plurality of suspect devices includes: providing aplurality of network parameters for the plurality of suspect devices toa neural network machine learning model to calculate the noise scoresfor the plurality of suspect devices.